In recent years, food adulteration of various kinds has become a severe problem in food safety detection. In order to get rid of the limitations of traditional qualitative identification of new food adulteration, Fourier transform near-infrared spectroscopy (FT-NIR) was used to collect the spectrum ranging from 12 400 to 4 000 cm-1. The pure ganoderma lucidum spore oil adulterated with peanut oil, corn oil, coix seed oil, and hogwash oil were investigated in this study, where the ganoderma lucidum spore oil adulterated with hogwash oil was taken as the new category of food adulteration. Then, Multiple Relevance Vector Machine (RVM) classifiers were constructed with calibration samples of the first 4 categories. The prediction samples and ganoderma lucidum spore oil adulterated with hogwash oil were discriminated by the 4 kinds of classifier. In addition, the discriminated results were further verified with new clustering algorithm. Results showed that the discriminant accuracy of the first four categories was close to 93.75% with RVM classifier, but the ganoderma lucidum spore oil adulterated with hogwash oil was mistaken for pure ganoderma lucidum spore oil because of the limitations of model. So a new clustering algorithm based on local density and distance decision graph was applied to verify that. It was found that the cluster centers were 1 when the samples only contained pure ganoderma lucidum spore oil, however, the cluster centers were 2 when the samples mixed with pure ganoderma lucidum spore and adulterated with hogwash oil. The results demonstrated the FT-NIR in combination with RVM classifier and new clustering algorithm could be used for the identification of the adulterant in the pure ganoderma lucidum spore oil and qualitatively identify new category of food adulteration, providing a new method to solve the problem of food diversified adulteration.